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  <div class="section" id="mindspore-nn-batchnorm3d">
<h1>mindspore.nn.BatchNorm3d<a class="headerlink" href="#mindspore-nn-batchnorm3d" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="mindspore.nn.BatchNorm3d">
<em class="property">class </em><code class="sig-prename descclassname">mindspore.nn.</code><code class="sig-name descname">BatchNorm3d</code><span class="sig-paren">(</span><em class="sig-param">num_features</em>, <em class="sig-param">eps=1e-5</em>, <em class="sig-param">momentum=0.9</em>, <em class="sig-param">affine=True</em>, <em class="sig-param">gamma_init='ones'</em>, <em class="sig-param">beta_init='zeros'</em>, <em class="sig-param">moving_mean_init='zeros'</em>, <em class="sig-param">moving_var_init='ones'</em>, <em class="sig-param">use_batch_statistics=None</em>, <em class="sig-param">data_format='NCDHW'</em><span class="sig-paren">)</span><a class="headerlink" href="#mindspore.nn.BatchNorm3d" title="Permalink to this definition">¶</a></dt>
<dd><p>对输入的五维数据进行批归一化层(Batch Normalization Layer)。</p>
<p>归一化在卷积网络中得到了广泛的应用。该层在5维输入（带有附加通道维度的mini-batch 三维输入）上应用批归一化，避免内部协变量偏移。</p>
<div class="math notranslate nohighlight">
\[y = \frac{x - \mathrm{E}[x]}{\sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta\]</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>BatchNorm的实现在图模式和PyNative模式下是不同的，因此不建议在网络初始化后更改其模式。</p>
<p>需要注意的是，更新running_mean和running_var的公式为 <span class="math notranslate nohighlight">\(\hat{x}_\text{new} = (1 - \text{momentum}) \times x_t + \text{momentum} \times \hat{x}\)</span> ,其中 <span class="math notranslate nohighlight">\(\Hat{x}\)</span> 是估计的统计量， <span class="math notranslate nohighlight">\(x_t\)</span> 是新的观察值。</p>
</div>
<p><strong>参数：</strong></p>
<ul class="simple">
<li><p><strong>num_features</strong> (int) - 指定输入Tensor的通道数量。输入Tensor的size为(N, C, D, H, W)。</p></li>
<li><p><strong>eps</strong> (float) - 确保数值稳定加在分母上的值。默认值：1e-5。</p></li>
<li><p><strong>momentum</strong> (float) - 动态均值和动态方差所使用的动量。默认值：0.9。</p></li>
<li><p><strong>affine</strong> (bool) - bool类型。设置为True时，可以学习gama和beta。默认值：True。</p></li>
<li><p><strong>gamma_init</strong> (Union[Tensor, str, Initializer, numbers.Number]) - gama参数的初始化方法。str的值引用自函数 <cite>initializer</cite> ，包括’zeros’、’ones’等。默认值：’ones’。</p></li>
<li><p><strong>beta_init</strong> (Union[Tensor, str, Initializer, numbers.Number]) - beta参数的初始化方法。str的值引用自函数 <cite>initializer</cite> ，包括’zeros’、’ones’等。默认值：’zeros’。</p></li>
<li><p><strong>moving_mean_init</strong> (Union[Tensor, str, Initializer, numbers.Number]) - 动态均值和动态方差所使用的动量。平均值的初始化方法。str的值引用自函数 <cite>initializer</cite> ，包括’zeros’、’ones’等。默认值：’zeros’。</p></li>
<li><p><strong>moving_var_init</strong> (Union[Tensor, str, Initializer, numbers.Number]) - 动态均值和动态方差所使用的动量。方差的初始化方法。str的值引用自函数 <cite>initializer</cite> ，包括’zeros’、’ones’等。默认值：’ones’。</p></li>
<li><p><strong>use_batch_statistics</strong> (bool) - 如果为True，则使用当前批次数据的平均值和方差值。如果为False，则使用指定的平均值和方差值。如果为None，训练时，将使用当前批次数据的均值和方差，并更新动态均值和方差，验证过程将直接使用动态均值和方差。默认值：None。</p></li>
<li><p><strong>data_format</strong> (str) - 数据格式的可选值为’NCDHW’。默认值：’NCDHW’。</p></li>
</ul>
<p><strong>输入：</strong></p>
<ul class="simple">
<li><p><strong>x</strong> (Tensor) - 输入shape为 <span class="math notranslate nohighlight">\((N, C_{in}, D_{in}, H_{in}, W_{in})\)</span> 的tensor。</p></li>
</ul>
<p><strong>输出：</strong></p>
<p>Tensor，归一化后的tensor，shape为 <cite>(N, C_{out}, D_{out},H_{out}, W_{out})</cite> 。</p>
<p><strong>异常：</strong></p>
<ul class="simple">
<li><p><strong>TypeError</strong> - <cite>num_features</cite> 不是整数。</p></li>
<li><p><strong>TypeError</strong> - <cite>eps</cite> 不是浮点数。</p></li>
<li><p><strong>ValueError</strong> - <cite>num_features</cite> 小于1。</p></li>
<li><p><strong>ValueError</strong> - <cite>momentum</cite> 不在范围[0, 1]内。</p></li>
<li><p><strong>ValueError</strong> - <cite>data_format</cite> 不是’NCDHW’。</p></li>
</ul>
<p><strong>支持平台：</strong></p>
<p><code class="docutils literal notranslate"><span class="pre">Ascend</span></code> <code class="docutils literal notranslate"><span class="pre">GPU</span></code> <code class="docutils literal notranslate"><span class="pre">CPU</span></code></p>
<p><strong>样例：</strong></p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">mindspore.nn</span> <span class="k">as</span> <span class="nn">nn</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">mindspore</span> <span class="kn">import</span> <span class="n">Tensor</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">net</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm3d</span><span class="p">(</span><span class="n">num_features</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">x</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">([</span><span class="mi">16</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">])</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">))</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">output</span> <span class="o">=</span> <span class="n">net</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">output</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="go">(16, 3, 10, 32, 32)</span>
</pre></div>
</div>
</dd></dl>

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